Machine Learning Interview Questions and Answers
Question - 21 : - What is SVM in machine learning? What are the classification methods that SVM can handle?
Answer - 21 : -
SVM stands for Support Vector Machine. SVM are supervised learning models with an associated learning algorithm which analyze the data used for classification and regression analysis.
The classification methods that SVM can handle are:
- Combining binary classifiers
- Modifying binary to incorporate multiclass learning
Question - 22 : - What do you mean by Genetic Programming?
Answer - 22 : -
Genetic Programming (GP) is almost similar to an Evolutionary Algorithm, a subset of machine learning. Genetic programming software systems implement an algorithm that uses random mutation, a fitness function, crossover, and multiple generations of evolution to resolve a user-defined task. The genetic programming model is based on testing and choosing the best option among a set of results.
Question - 23 : - Describe the classifier in machine learning.
Answer - 23 : -
A classifier is a case of a hypothesis or discrete-valued function which is used to assign class labels to particular data points. It is a system that inputs a vector of discrete or continuous feature values and outputs a single discrete value, the class.
Question - 24 : - What do you understand by algorithm independent machine learning?
Answer - 24 : -
Algorithm independent machine learning can be defined as machine learning, where mathematical foundations are independent of any particular classifier or learning algorithm.
Question - 25 : - What are the functions of Unsupervised Learning?
Answer - 25 : -
- Finding clusters of the data
- Finding low-dimensional representations of the data
- Finding interesting directions in data
- Finding novel observations/ database cleaning
- Finding interesting coordinates and correlations
Question - 26 : - What are the functions of Supervised Learning?
Answer - 26 : -
- Classification
- Speech Recognition
- Regression
- Predict Time Series
- Annotate Strings
Question - 27 : - What do you understand by Decision Tree in Machine Learning?
Answer - 27 : -
Decision Trees can be defined as the Supervised Machine Learning, where the data is continuously split according to a certain parameter. It builds classification or regression models as similar as a tree structure, with datasets broken up into ever smaller subsets while developing the decision tree. The tree can be defined by two entities, namely decision nodes, and leaves. The leaves are the decisions or the outcomes, and the decision nodes are where the data is split. Decision trees can manage both categorical and numerical data.
Question - 28 : - Describe Precision and Recall?
Answer - 28 : -
Precision and Recall both are the measures which are used in the information retrieval domain to measure how good an information retrieval system reclaims the related data as requested by the user.
Precision can be said as a positive predictive value. It is the fraction of relevant instances among the received instances.
On the other side, recall is the fraction of relevant instances that have been retrieved over the total amount or relevant instances. The recall is also known as sensitivity.
Question - 29 : - What are the necessary steps involved in Machine Learning Project?
Answer - 29 : -
There are several essential steps we must follow to achieve a good working model while doing a Machine Learning Project. Those steps may include parameter tuning, data preparation, data collection, training the model, model evaluation, and prediction, etc.
Question - 30 : - What do you understand by ILP?
Answer - 30 : -
ILP stands for Inductive Logic Programming. It is a part of machine learning which uses logic programming. It aims at searching patterns in data which can be used to build predictive models. In this process, the logic programs are assumed as a hypothesis.